|
|
|
|
|
|
by pdyc
171 days ago
|
|
|
I like your approach of "smart routing" but using regex/keywords based approach has a problem that it does not captures semantic similarity of keywords so search with similar intents are missed, how are you handling it? or you dont need to handle it since it is for domain experts and they are likely to search based on keywords(dictionary)? |
|
|
Currently, I handle this via Smart Routing. The engine analyzes the intent of your query (e.g. identifying if you’re looking for an RCT, a specific guideline, or drug dosing) and routes it to the most relevant clinical database using high-precision keyword matching.
I chose this deterministic approach for the launch to ensure clinical precision. While vector/semantic search is great for general concepts, it can sometimes surface 'similar-ish' papers that miss the specific medical nuances (like a specific ICD-10 code or dosage) required for clinical evidence.
The LLM (Gemini 2.5 Flash) currently lives in the Synthesis Layer. It takes the raw, high-precision results and synthesizes them into the clinical summaries you see.
I actually have LLM-based query expansion (translating natural language into robust MeSH/Boolean strings) built into the infrastructure, but I am keeping it in 'staging' right now. I want to ensure that as I bridge that semantic gap, I don't sacrifice the deterministic accuracy that medical professionals expect.